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Quantification of Sensory Abnormalities

Quantification of Sensory Abnormalities. Client: Dr. Miroslav Backonja Advisor: Prof. Mitchell Tyler. Group Members (in order of appearance): Colleen Farrell (BSAC), Adam Pala (BWIG), Jeremy Schaefer (Leader), Steve Wyche (Communication). Presentation Overview. I. Background Information

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Quantification of Sensory Abnormalities

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  1. Quantification of Sensory Abnormalities Client: Dr. Miroslav Backonja Advisor: Prof. Mitchell Tyler Group Members (in order of appearance): Colleen Farrell (BSAC), Adam Pala (BWIG), Jeremy Schaefer (Leader), Steve Wyche (Communication)

  2. Presentation Overview • I. Background Information • II. Problem Statement • III. Client Requirements • IV. Data Analysis • V. Design Alternatives • VI. Design Matrix • VII. Final Design and Future Work

  3. I. Background Information • Neuropathic Pain • cutaneous abnormality • sharp pain, tingling, burning, numbness • Record changes in sensation • Tracing paper currently used • Accurate, repeatable method needed • No commercial competition

  4. II. Problem Statement Dr. Miroslav Backonja, a neurologist who works in pain medicine at UW Hospital, has expressed the need for a more accurate method to measure the surface area of cutaneous sensory abnormalities. Currently, tracing paper is used to trace the affected area and a plenimeter is used to measure surface area. Dr. Backonja is looking to be able to measure surface area on contoured regions of the body in a more accurate and repeatable manner.

  5. III. Client Requirements Our client, Dr. Backonja, has specified the following constraints regarding our design prototype: • Minimally invasive • Accurate measurement (acceptable error rate: 5–10%) • Cost effective • Consistent, reproducible results • Clinical use • Data should be collected and displayed • Under $1000 if possible

  6. IV. Data Analysis • Once the data is collected as points having three spatial dimensions (i.e. , for points i=1 to i=N), it is sent to MATLAB

  7. (IV. Data Analysis – continued) • The interpolated surfaces can be visualized using MATLAB’s 3D “tri” functions (which employ the Delaunay triangulation algorithm)

  8. (IV. Data Analysis – continued) • Finally, the surface area is calculated using Heron’s Formula

  9. V. Design Alternatives • Active Infrared • Passive Infrared • Laser

  10. Active Infrared IR Cameras 850nm LED http://z.about.com/d/firstaid/1/0/e/-/-/-/Arm02.jpg http://www.phasespace.com/productsMain.html

  11. Advantages Highly Accurate ( < 1mm) Data capture easy and fast Disadvantages Very high initial cost (~$20,000) LED must be moved across the patient’s skin LED must be pointed to camera Active Infrared (continued)

  12. Passive Infrared IR Cameras 3/16” diameter reflective ball http://www.naturalpoint.com/optitrack/products/flex-v100/inthebox.html http://z.about.com/d/firstaid/1/0/e/-/-/-/Arm02.jpg

  13. Passive Infrared (continued) • Advantages • Highly Accurate • Data capture easy and fast • Cheaper than Active Infrared (~$2,000 compared to ~$20,000) • Disadvantages • High initial cost • Reflective ball must be moved across the patient’s skin

  14. Laser • Advantages • Inexpensive ($130) • No direct contact with patient • Disadvantages • Difficult to use • Time consuming • Less Accurate (~2mm) Distance to point Distance to “origin” Ѳ Φ http://www.amazon.com/Spectra-Precision-HD50-Handheld-Distance/dp/B000Q5U9JG

  15. VI. Design Matrix

  16. VII. Future Work • Obtain IR camera system • Design and create pen • Create and refine algorithms • Modeling “overlapped” surfaces • Put system together • Testing • Systematize the procedure

  17. Questions?

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